About Us

Sebők, Miklós

Miklós Sebők is Research Fellow and Head of Department at the Centre of Social Sciences, Hungarian Academy of Sciences in Budapest. He earned an M.A. degree in politics at the University of Virginia and an M.A. degree in economics at the Corvinus University of Budapest. He received his Ph.D. in Political Science from ELTE University of Budapest. He currently serves as the Research director of the Hungarian Comparative Agendas Project. His research interests include political economy and public policy, as well as quantitative text analysis and the application of other Big Data methods to social science problems. He has published articles in, inter alia, the Journal of Comparative Politics, Journal of Public Budgeting, Accounting and Financial Management, Japanese Journal of Political Science and Intersections – East European Journal of Society and Politics. He is the author of “Hatalom szabályok nélkül” (“Power without rules” – Új Mandátum Publishing, 2014), a comparative examination of the impact of the financial crisis on bureaucratic structure, for which he received the Kolnai Prize for best Hungarian language publication in political science.


Research at iASK

Dynamic Agenda Representation in Central and Eastern Europe: A Comparative Big Data Analysis

The attention of scholars of representation has long been captured by the concept of elections as the key mechanism in establishing cohesiveness between the electorate and political elites. Nevertheless, representation is also achieved by means of the constant flow of what is called policy responsiveness (or dynamic agenda representation), the process of between-election adjustments and feedbacks between policymakers and the “thermostat” of public opinion. The project engages with both the methodological and the comparative limitations of extant research on dynamic agenda representation. It uses existing Big Data databases and compiles new ones in order to apply the theoretical framework to Central and Eastern Europe (CEE). Algorithm-based topic modelling techniques are utilized in order to to code various sources of data (to be discussed below) for policy topics, such as education or health. The project also offers a useful tool for policy-makers in distinguishing breakdowns in the congruence of various policy venues and, therefore, for the implementation of timely interventions in policy areas with higher than average agendas share in public opinion and media.